There have been efforts to solve the overlap problem in speaker diarization, by leveraging a separate overlap detection module that identifies segments containing overlapped speech. This overlap detection may be done using hidden Markov models (HMMs) [1]}, [2]} or using neural networks [3]}, [4]}. Additional speaker labels may be assigned to the overlapping segments once detected [5]}. An alternate framework that is popular in recent years applies end-to-end systems trained with supervision (often using simulated mixtures). This includes models such as end to end neural diarization(EEND) [6]} or region proposal networks (RPN) [7]}, which can naturally handle overlaps, and have shown promising results on challenging data sets.
